Accurate-Mass Databases for Comprehensive Screening of Pesticide

Anal. Chem. 2009, 81, 913–929

Accurate-Mass Databases for Comprehensive Screening of Pesticide Residues in Food by Fast Liquid Chromatography Time-of-Flight Mass Spectrometry Milagros Mezcua,† Octavio Malato,† Juan F. Garcıá-Reyes,‡ Antonio Molina-Dıáz,‡ and Amadeo R. Ferna ´ ndez-Alba*,† Community Reference Laboratory (DG SANCO) for Residues of Pesticides in Fruits and Vegetables, Pesticide Residue Research Group, Department of Hydrogeology and Analytical Chemistry, University of Almerıá, 04120 La Canãda de San Urbano, Almerıá, Spain, and Analytical Chemistry Research Group, Department of Physical and Analytical Chemistry, University of Jaeń, 23071 Jaeń, Spain Because of the international trade of fruits and vegetables and the lack of harmonized regulations on the use of pesticides worldwide, the development of comprehensive screening methods for analyzing hundreds of pesticides and other banned chemicals is very convenient. This work reports the development and evaluation of a rapid automated screening method for determining pesticide residues in food using liquid chromatography electrospray time-of-flight mass spectrometry (LC-TOFMS) based on the use of an accurate-mass database. The database created includes data not only on the accurate masses of the target ions but also the characteristic in-source fragment ions (over 400 fragments included) and retention time data. This customized database was associated to commercially available software which extracted all the potential compounds of interest from the LC-TOFMS raw data of each sample and matched them against the database to search for targeted compounds in the sample. This automatic screening method requires a careful optimization of the accurate-mass window and retention time tolerances, which play a determinant role on the selectivity, accuracy, and throughput of the whole procedure. Values of 10 mDa for preliminary screening and 1 mDa/5 ppm for confirmation along with a (0.15 min retention time window were found to be optimum for the compounds and samples tested. The optimized methods enable the automated screening of ca. 300 compounds in less than 20 min including the LC-MS run and data processing. The proposed method was applied to 60 real samples, and the results of the positive findings compared well with those obtained using a liquid chromatography tandem mass spectrometry (LC-MS/MS) method (triple quadrupole). The rates obtained on the identification of compounds in spiked and real samples in an automated fashion at different concentration levels were over 95% * To whom correspondence should be addressed. Phone: (+34) 950015034. Fax: (+34) 950014102. E-mail: [email protected]. † University of Almerıá. ‡ University of Jaeń. 10.1021/ac801411t CCC: $40.75  2009 American Chemical Society Published on Web 12/31/2008

of the compounds, thus revealing as a convenient tool for the large-scale screening of pesticides in foodstuffs. The use of agrochemicals at various stages of cultivation has an important impact in food protection and quality preservation. For this reason, there are different regulations on the use of pesticides for each country, and these lists are not harmonized worldwide though. In addition, recent sanitary European alerts have pointed out problems related with the detection of illegal or misused pesticides in various crops. This fact is becoming more important since the number of pesticides authorized in Europe (Annex I, Directive 91/414/EEC) has been reduced to around 50% of total amount of compounds manufactured.1 The practical “target analysis” approach applied in many routine laboratories on pesticide residue testing consists of selecting a list of around 100-150 of gas chromatography (GC) and liquid chromatography (LC) amenable compounds by using the established priority list combined. This fact means that the majority of the low-frequency or misused compounds are not sought. It is desirable that this target analysis approach implemented in official routine laboratoriessbased on lists of common authorized pesticidessshould be extended to those pesticides commonly used in other countries but out of the Annex I or even those illegal compounds that could be commercialized on the “black market”.2 From an analytical point of view, this task is difficult to tackle since involves extending the scope of the multiresidue methods to several hundreds of chemicals. Besides, it is difficult to carry out such approaches cost-effectively due to the time and money required when upgrading methods by incorporating new compounds. In addition, the management of these standard solutions, the extra method-upgrade efforts, and overall laboratory throughput decrease associated are also important drawbacks. Therefore, other alternatives should be explored and evaluated. The first option is undoubtedly the application of combined analysis based on a target/screening approach, which is to target a group of (1) Council Directive of 15 July 1991 (91/414/EEC) concerning the placing of plant protection products on the market (OJ L 230 19.8.1991).p1. (2) Mezcua, M.; Ferrer, C.; Garcia-Reyes, J. F.; Martıńez-Bueno, M. J.; Albarracıń, M.; Claret, M.; Fernańdez-Alba, A. R. Rapid Commun. Mass Spectrom. 2008, 22, 1384–1392.

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priority compounds based in their toxicity and/or frequency of detection and at the same time rapid large-scale screening of a large amount of compounds just with identification purposes.3-6 Up to date, (polar) pesticide residue analysis in food has been accomplished by liquid chromatography tandem mass spectrometry (LC-MS/MS) in the multiple reaction monitoring (MRM) mode.7-9 This approach has a severe limitation that is the number of compounds that can be screened in a single run.7-9 Both detection limits and chromatographic peak shape are sacrificed as the number of target compounds increases. Up to 150-200 compounds (depending on the scan speed/dwell-time) can be analyzed in a run by LC-MS/MS in the MRM mode with a dedicated chromatographic method. In addition, when increasing the number of compounds included in the MRM method, the possibility of finding common or overlapped transitions for coeluting isobaric compounds rises. This fact is underestimated so far in published methods and should be studied in detail when dealing with large-scale multiresidue methods.10 Besides, another major limitation of these MRM methods is that they are blind to compounds not defined in the MRM method (nontarget analysis) so that no or scarce information on possible nontarget or unknown pesticides or their degradation products is available when using these techniques. Unlike gas chromatography/mass spectrometry (GC/MS) reverse-search methods based on National Institute for Standards and Testing (NIST) (electron impact) pesticide libraries,11 one of the main lacks traditionally reported in the use of LC-MS is the unavailability of commercial pesticide libraries which allows a rapid screening of the samples as can be performed in GC/MS. The universal applicability of mass spectral libraries has been hampered by the scarce reproducibility of in-source collision-induced dissociation (CID) spectra and the difficulty of interchanging spectraacquiredwithinstrumentsfromdifferentmanufacturers.7,12-14 In contrast, accurate mass measurements are almost specific and universal for every target analyte regardless the instrumentation used. In this sense, liquid chromatography electrospray time-offlight mass spectrometry (LC-TOFMS) is a cost-effective technique for performing routine accurate mass analysis based on (3) Garcia-Reyes, J. F.; Hernando, M. D.; Molina-Dıáz, A.; Fernańdez-Alba, A. R. Trends Anal. Chem. 2007, 26, 828–841. (4) Garcia-Reyes, J. F.; Hernando, M. D.; Ferrer, C.; Molina-Dıáz, A.; FernańdezAlba, A. R. Anal. Chem. 2007, 79, 7308–7323. (5) Ferrer, I.; Fernańdez-Alba, A. R.; Zweigenbaum, J. A.; Thurman, E. M. Rapid Commun. Mass Spectrom. 2006, 20, 3659–3668. (6) Thurman, E. M.; Ferrer, I.; Malato, O.; Fernańdez-Alba, A. R. Food Addit. Contam. 2006, 23, 1169–1178. (7) Alder, L.; Greulich, K.; Kempe, G.; Vieth, B. Mass Spectrom. Rev. 2006, 25, 838–865. (8) Soler, C.; Pico´, Y. Trends Anal. Chem. 2007, 26, 103–115. (9) Hernańdez, F.; Pozo, O. J.; Sancho, J. V.; Bijlsma, L.; Barreda, M.; Pitarch, E. J. Chromatogr., A 2006, 1109, 242–252. (10) Kmella´r, B.; Fodor, P.; Pareja, L.; Ferrer, C.; Martıńez-Uroz, M. A.; Valverde, A.; Fernandez-Alba, A. R. J. Chromatogr., A 2008, 1215, 37-50. (11) Wylie, P. L. Screening of 926 Pesticides and Endocrine Disruptors by GC/ MS with Deconvolution Separating Software and a New Pesticide Library; Agilent Application Note 598-5076EN, W. D., 2006. (12) Saint-Marcoux, F.; Lachaˆtre, G.; Marquet, P. J. Am. Soc. Mass Spectrom. 2003, 14, 14–22. (13) Gergov, M.; Weinmann, W.; Meriluoto, J.; Uusitalo, J.; Ojanpera, I. Rapid Commun. Mass Spectrom. 2004, 18, 1039–1046. (14) Mueller, C. A.; Weinmann, W.; Dresen, S.; Scheiberg, A.; Gregov, M. Rapid Commun. Mass Spectrom. 2005, 19, 1332–1338.

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target databases.15 The main features of LC-TOFMS instruments are accurate mass analysis capabilities16 and high sensitivity in “full-scan” acquisition mode so that pesticides can be detected in complex matrixes at low picogram levels. Unambiguous identification is accomplished by means of accurate mass measurements from (de)protonated molecules, in-source CID fragment ions, and isotope signature matching.3,17,18 In addition, LC-TOFMS provides satisfactory analytical performance for quantitation purposes, as has been demonstrated so far in the literature.3,4,19,20 Since LC-TOFMS has the ability to record an unlimited number of compounds because it operates in full-scan mode, this technique is very convenient for the development of screening strategies based on the use of accurate-mass databases. Preliminary work has been accomplished by our group, which includes manual searching of empirical formulas obtained by accurate mass measurements in available commercial or custom databases18,21 and databases based on accurate mass measurement with optional retention time, showing promising results although those studies required an extensive manual searching work and were only accomplished for a limited number of compounds and samples.5,6 This work reports the development and evaluation of a rapid automated screening method for the detection of pesticide residues in food using fast LC-TOFMS, based on the use of an accurate-mass database. The database created includes accurate masses of the target ions, their characteristic in-source fragment ions (over 400 fragments included), isotopic signature information, and retention time data. This database was associated to software which extracts all the compounds of interest from the LC-TOFMS raw data of each sample and matches them against the database to search for targeted compounds in the sample. The number of compounds that can be screened in an LC-TOFMS run can be easily upgraded (nontarget capabilities), thus enabling the reevaluation of the recorded data. The developed method was applied to the analysis of over 60 fruit and vegetable samples, and the results obtained were evaluated and compared with those from an LC-MS/MS method. EXPERIMENTAL SECTION Chemicals and Reagents. Pesticide analytical standards were purchased from Dr. Ehrenstorfer GmbH. (Ausburg, Germany) or Riedel-de-Hae¨n (Seelze, Germany). Individual pesticide stock solutions (ca. 500 µg mL-1) were prepared in pure methanol or ethyl acetate and were stored at -18 °C. HPLC-grade acetonitrile, ethyl acetate, and methanol were obtained from Merck (Darmstadt, Germany). Formic acid was obtained from Fluka (Buchs, Switzerland). A Milli-Q-Plus ultrapure water system from Millipore (Milford, MA) was used throughout the study (15) Ojanpera¨, S.; Pelander, A.; Pelzing, M.; Kubs, I.; Vuori, E.; Ojanpera¨, I. Rapid Commun. Mass Spectrom. 2006, 20, 1161–1167. (16) Ferrer, I.; Garcia-Reyes, J. F.; Fernańdez-Alba, A. R. Trends Anal. Chem. 2005, 24, 671–682. (17) Lacorte, S.; Fernańdez-Alba, A. R. Mass Spectrom. Rev. 2006, 25, 866– 880. (18) Garcia-Reyes, J. F.; Ferrer, I.; Thurman, E. M.; Molina-Dıáz, A.; FernańdezAlba, A. R. Rapid Commun. Mass Spectrom. 2005, 19, 2780–2788. (19) Pico´, Y.; La Farre´, M.; Soler, C.; Barcelo´, D. J. Chromatogr., A 2007, 1176, 123–134. (20) Garcia-Reyes, J. F.; Molina-Dıáz, A.; Fernańdez-Alba, A. R. Anal. Chem. 2007, 79, 307–321. (21) Thurman, E. M.; Ferrer, I.; Fernańdez-Alba, A. R. J. Chromatogr., A 2005, 1067, 127–134.

to obtain the HPLC-grade water used during the analyses. PSA (primary-secondary amine) was obtained from Supelco. Sample Treatment. Fruit and vegetable samples were purchased from different local markets. The procedure (the so-called “QuEChERS”) described elsewhere22,23 comprised the following steps: a representative 15 g portion of previously homogenized sample was weighed in a 200 mL PTFE centrifuge tube. Then 15 mL of acetonitrile was added, and the tube was vigorously shaken for 1 min. After this time, 1.5 g of NaCl and 6 g of MgSO4 were added, and the shaking process was repeated for 1 min. The extract then was centrifuged (3700 rpm) for 1 min. An amount of 5 mL of the supernatant (acetonitrile phase) was then taken with a pipet and transferred to a 15 mL graduated centrifuge tube containing 250 mg of PSA and 750 mg of MgSO4, that was then energetically shaken for 20 s. The extract was then centrifuged again (3700 rpm) for 1 min. Finally, an extract containing the equivalent of 1 g of sample/mL in nearly 100% acetonitrile was obtained. An amount of 2 mL of this extract was then evaporated to near dryness and reconstituted to 2 mL of 10% acetonitrile. Prior to LC-MS analysis, the extract was filtered through a 0.45 µm PTFE filter (Millex FG, Millipore, Milford, MA). Liquid Chromatography. The separation of the pesticides from the whole fruit or vegetable extract was carried out using a high-performance liquid chromatography (HPLC) system (consisting of vacuum degasser, autosampler, and a binary pump) (Agilent series 1100, Agilent Technologies, Santa Clara, CA) equipped with a reversed-phase XDB-C18 analytical column of 4.6 mm × 50 mm and 1.8 µm particle size (Agilent Technologies, Santa Clara, CA). An amount of 20 µL of the sample extract was injected in each run. Mobile phases A and B were water/acetonitrile (95:5) (v/v) with 0.1% formic acid and acetonitrile/water (95:5) (v/v) with 0.1% formic acid. The chromatographic method held the initial mobile phase composition (10% B) constant for 1 min, followed by a linear gradient to 100% B up to 12 min, and kept for 5 min at 100% B. The flow rate used was 0.6 mL min-1. Time-of-Flight Mass Spectrometry. The HPLC system was connected to a time-of-flight mass spectrometer Agilent MSD TOF (Agilent Technologies, Santa Clara, CA) equipped with an electrospray interface operating in the positive ion mode, using the following operation parameters: capillary voltage, 4000 V; nebulizer pressure, 40 psig; drying gas flow rate, 9 L min-1; gas temperature, 325 °C; skimmer voltage, 60 V; octapole dc 1, 37.5 V; octapole rf, 250 V; fragmentor voltage (in-source CID fragmentation), 190, 210, and 230 V. LC-MS accurate mass spectra were recorded across the range of 50-1000 m/z. Accurate mass measurements of each peak from the total ion chromatograms were obtained using an automated calibrant delivery system to provide the correction of the masses. The instrument performed the internal mass calibration automatically, using a dual-nebulizer electrospray source with an automated calibrant delivery system, which introduces the flow from the outlet of the chromatograph together with a low flow (approximately 10 µL min-1) of a calibrating solution which contains the internal reference masses purine (C5H4N4 at m/z 121.050 873) (22) Anastassiades, M.; Lehotay, S. J.; Stajnbaher, D.; Schenk, F. J. J. AOAC Int. 2003, 86, 412–431. (23) Lehotay, S. J.; De Kok, A.; Hiemstra, M.; Van Bodegraven, P. J. AOAC Int. 2005, 88, 595–614.

and HP-0921 ([hexakis-(1H,1H,3H-tetrafluoropentoxy)-phosphazene] (C18H18O6N3P3F24) at m/z 922.009 798)). The instrument provided a typical resolution of 9700 ± 500 (m/z 922). The full-scan data recorded was processed with Applied Biosystems/MDS Sciex Analyst QS software (Frankfurt, Germany) with accurate mass application-specific additions from Agilent MSD TOF software and with Agilent Mass Hunter software (version B.01.03 Build 1.3.157.0 Patch 2). Construction of a Database Including Accurate Masses of Pesticides, Retention Time, and Characteristic Fragmentation. The selected 297 pesticides were divided in 10 mixtures with ∼30 pesticides each at a concentration of 300 ng mL-1 (each), and these solutions were injected in the LC-TOFMS system. The retention time, the theoretical exact mass, and the elemental composition of each pesticide were collected in an Excel sheet. Besides, the mass spectrum of each pesticide was carefully investigated, and the characteristic fragment ions (with relative abundance higher than 5-10%, using the default 190 V fragmentor voltage) of each pesticide were also included in the database (see Table S1, Supporting Information). For the automatic screening method, a part of the information (included in Table 1: molecular formula, retention time, theoretical exact mass, and compound name) was selected and an Excel spreadsheet was constructed containing the exact mass data for each of the pesticides and fragment ions and their retention times. This file was put into csv format for use by the Agilent TOF automated data analysis software (Qualitative Mass Hunter, version B.01.03 Build 1.3.157.0 Patch 2). The csv file is searched automatically by the LC-TOFMS instrument at the end of the sample run, and a report is generated on compounds that were found in the database. In addition, the Excel table built was converted to Access format, and a custom database was generated in which different parameters can be used as searching parameters; these parameters are pesticide name, molecular formula, chemical structure, accurate mass, and retention time (see Figure S1, Supporting Information). Automatic Screening Using the Pesticide Database. When a sample is processed with the searching database tool of the software (Qualitative Mass Hunter), two steps are performed: (1) extraction of the compounds (molecular feature extraction (MFE)) from the raw data (full-scan TOFMS positive ion mode spectra) and (2) database searching. The first one is a search for compounds by molecular feature, in which peak filter higher or equal to 100 counts was chosen for the ion extraction. A relative abundance higher or equal to 0.010% and an absolute abundance higher or equal to height of 1000 counts were selected as compounds filters. The second step is to identify and search for the presence of target compounds in the sample. In this step the csv Excel file created is employed as database. The defined search criteria are accurate mass tolerances and retention time tolerance. Two mass tolerances were selected for two-step confirmation: 10 mDa (for screening purposes) and 5 ppm and/or 1 mDa (confirmation). The retention time tolerance was fixed at ±0.15 min in both steps. Application of the Automated Screening Method for the Identification and Confirmation of Pesticides in Fruit and Vegetable Real Samples. Over 60 fruit and vegetable market samples were extracted by QuEChERS and analyzed by the Analytical Chemistry, Vol. 81, No. 3, February 1, 2009

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Table 1. Accurate-Mass Database of the Studied Pesticides Including Retention Times (RT), Theoretical Accurate Masses, and Elemental Compositions of the Detected Ions no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 916

ion name acephate acetamiprid acetochlor aclonifen acrinathrin alachlor alanycarb albendazole aldicarb aldicarb sulfone aldicarb sulfoxide ametryn amitraz anilazine anilofos atrazine azamethiphos azinphos-methyl azinphos-ethyl azoxystrobin benalaxyl bendiocarb bensulfuron-methyl bensultap benzoximate bifenox bromacil bromoxynil bromuconazole bupirimate buprofezin butocarboxim butoxycarboxim buturon cambendazole carbaryl carbendazim carbetamide carbofuran carbofuran-3-hydroxy carbosulfan chlorbromuron chlorfenapyr chlorfenvinphos chlorfluazuron chloridazon chlortoluron chloroxuron chlorpropham chlorsulfuron chromafenozide cinosulfuron clethodim clofentezine clomazone coumaphos coumaphos oxon cyanofenphos cycloate cymoxanil cyproconazole cyprodinil cyromazine daminozide deet deltamethrin demeton S-methyl desethyl terbutylazine diafenthiuron dialifos diazinon dichlofluanid dichlorvos

RT (min) 1.90 6.69 10.94 11.17 13.46 10.90 10.80 7.36 7.48 4.79 3.41 7.43 12.65 10.23 11.41 8.74 7.94 9.82 10.82 10.11 11.34 8.37 9.13 8.74 12.03 11.82 7.59 8.84 9.98 9.36 11.35 7.06 4.79 9.39 6.17 8.56 4.15 7.36 8.41 6.1 14.63 10.1 12.3 11.08 12.74 6.34 8.50 9.81 10.43 8.43 10.65 8.02 9.87 11.67 9.42 11.53 9.31 11.52 12.25 3.69 9.72 9.41 1.00 1.01 8.77 13.5 7.97 7.82 13.27 11.96 11.73 11.18 7.83

m/z calcd 184.0192 223.0745 270.1255 265.0374 564.1216 270.1255 400.1359 266.0958 213.0667 223.0747 207.0798 228.1277 294.1965 274.9653 368.0305 216.1010 324.9809 339.9949 346.0444 404.1241 326.1751 224.0917 411.0969 432.0426 364.0946 341.9930 261.0233 275.8654 375.9612 317.1642 306.1634 213.0668 223.0747 237.0789 303.0910 202.0862 192.0768 237.1234 222.1125 238.1074 381.2206 292.968692 406.9768 358.9768 539.9702 222.0429 213.0789 291.0895 236.0449 358.0371 395.2329 414.1077 360.1394 303.0198 240.0786 363.0217 347.0445 304.0555 216.1416 199.0826 292.1211 226.1339 167.1040 161.0920 192.1383 525.9623 253.0092 202.0853 385.2308 394.0097 305.1083 332.9696 220.9532


ion [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

elemental compositions +

H] H]+ H]+ H]+ Na]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+

C4H11NO3PS C10H12ClN4 C14H21ClNO2 C12H10ClN2O3 C26H21F6NO5Na C14H21ClNO2 C17H26N3O4S2 C12H16N3O2S C7H14N2O2SNa C7H15N2O4S C7H15N2O3S C9H18N5S C19H24N3 C9H6Cl3N4 C13H20ClNO3PS2 C8H15ClN5 C9H11ClN2O5PS C10H12N3O3PS2Na C12H17N3O3PS2 C22H18N3O5 C20H24NO3 C11H14NO4 C16H19N4O7S C17H22NO4S4 C18H19ClNO5 C14H10Cl2NO5 C9H14BrN2O2 C7H4Br2NO C13H13BrCl2N3O C13H25N4O3S C16H24N3OS C7H14N2O2SNa C7H15N2O4S C12H14ClN2O C14H15N4O2S C12H12NO2 C9H10N3O2 C12H17N2O3 C12H16NO3 C12H16NO4 C20H33N2O3S C9H11BrClN2O2 C15H12BrClF3N2O C12H15Cl3O4P C20H10Cl3F5N3O3 C10H9ClN3O C10H14ClN2O C15H16ClN2O2 C10H12ClNO2Na C12H13ClN5O4S C24H31N2O3 C15H20N5O7S C17H27ClNO3S C14H9Cl2N4 C12H15ClNO2 C14H17ClO5PS C14H17ClO6P C15H15NO2PS C11H22NOS C7H11N4O3 C15H19ClN3O C14H16N3 C6H11N6 C6H13N2O3 C12H18NO C22H19Br2NO3Na C6H15O3PS2Na C7H13ClN5 C23H33N2OS C14H18ClNO4PS2 C12H22N2O3PS C9H12Cl2FN2O2S2 C4H8Cl2O4P

commentsa

ICS_F HMW ICS_F HMW SC ICS_F ICS_F SC HMW HMW HMW, ICS_IP HMW SC HMW HMW HMW HMW HMW SC HMW, SC ICS_F SC ICS_F

HMW HMW, ICS_IP HMW

HMW HMW, ICS_RES HMW HMW, ICS_IP HMW HMW

ICS_IP, SC

HMW ICS_F HMW HMW SC

Table 1. Continued no. 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147

ion name diclobutrazol dicloran dicrotophos diethofencarb difenoconazole difenoxuron diflubenzuron dimefuron dimethoate dimethomorph dimethylvinphos diniconazole dinocap dinotefuran disulfoton diuron dodemorph edifenphos emamectin benzoate epoxiconazole ethiofencarb ethion ethiprole ethoxyquin etofenprox etrimfos fenamiphos fenarimol fenazaquin fenbendazole fenbuconazole fenfuram fenhexamid fenitrothion fenobucarb fenoxaprop-ethyl fenoxycarb fenpropathrin fenpropidin fenpropimorph fenthion (1) fenthion (2) fenuron flamprop flamprop-methyl flazasulfuron florasulam fluacrypyrim fluazifop-butyl flufenacet flufenoxuron fludioxonil fluometuron fluoroacetamide fluquinconazole fluridone fluroxypyr flurtamone flusilazole folpet fonofos forchlorfenuron formetanate fosthiazate fuberidazole haloxyfop haloxyfop etotyl haloxyfop methyl hexaconazole hexaflumuron hexythiazox imazalil imazapyr imazaquin

RT (min) 10.31 9.56 5.28 9.88 11.24 8.89 10.49 9.23 6.61 9.30 10.02 10.84 13.27 3.08 11.95 8.80 7.85 11.01 9.95 10.04 8.8 12.74 9.62 8.33 14.09 11.53 9.97 9.92 13.14 8.36 10.37 8.89 10.24 10.72 9.69 12.05 10.72 13.5 8.20 8.24 9.56 11.36 6.27 9.09 10.51 9.30 8.41 12.02 12.61 10.87 12.62 9.75 8.59 1.05 10.25 9.54 7.73 9.77 10.27 10.57 11.72 8.45 0.97 8.55 4.60 10.43 12.25 11.77 10.50 11.62 12.97 7.45 5.56 7.83

m/z calcd 328.0978 206.9723 238.0839 268.1543 406.0720 287.1396 311.0393 339.1218 230.0075 388.1316 330.9455 326.0821 387.1526 203.1139 297.0177 233.0243 282.2791 311.0323 886.5311 330.0804 226.0896 384.9949 396.9899 218.1539 399.1930 293.0719 304.1131 331.0399 307.1804 300.0801 337.1215 202.0863 302.0709 278.0247 208.1332 362.0790 302.1387 350.1750 274.2529 304.2640 279.0273 279.0273 165.1028 322.0640 336.0797 408.0584 360.0373 427.1475 384.1417 364.0671 489.0441 249.0470 233.0896 78.0349 376.0163 330.1100 254.9734 334.1049 316.1076 295.9101 247.0375 248.0585 222.1237 284.0538 185.0709 362.0401 434.0976 376.0557 314.0821 460.9894 353.1085 297.0556 262.1186 312.1343

ion [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +


H] H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+

C15H20Cl2N3O C6H5Cl2N2O2 C8H17NO5P C14H22NO4 C19H18Cl2N3O3 C16H19N2O3 C14H10ClF2N2O2 C15H20ClN4O3 C5H13NO3PS2 C21H23ClNO4 C10H11Cl3O4P C15H18Cl2N3O C18H25N2O6 C7H15N4O3 C8H19O2PS3Na C9H11Cl2N2O C18H36NO C14H16O2PS2 C49H76NO13 C17H14ClFN3O C11H16NO2S C9H23O4P2S4 C13H10Cl2F3N4OS C14H20NO C25H28O3Na C10H18N2O4PS C13H23NO3PS C17H13Cl2N2O C20H23N2O C15H14N3O2S C19H18ClN4 C12H12NO2 C14H18ClNO2 C9H13NO5PS C12H18NO2 C18H17ClNO5 C17H20NO4 C22H24NO3 C19H32N C20H34NO C10H16O3PS2 C10H16O3PS2 C9H13N2O C16H14ClFNO3 C17H16ClFNO3 C13H13F3N5O5S C12H9F3N5O3S C20H22F3N2O5 C19H21F3NO4 C14H14F4N3O2S C21H12ClF6N2O3 C12H7F2N2O2 C10H12F3N2O C2H5FNO C16H9Cl2FN5O C19H15F3NO C7H6Cl2FN2O3 C18H15F3NO2 C16H16F2N3Si C9H5Cl3NO2S C10H16OPS2 C12H11ClN3O C11H16N3O2 C9H19NO3PS2 C11H9N2O C15H12ClF3NO4 C19H20ClF3NO5 C16H14ClF3NO4 C14H18Cl2N3O C16H9Cl2F6N2O3 C17H22ClN2O2S C14H15Cl2N2O C13H16N3O3 C17H18N3O3

commentsa

HMW HMW HMW HMW ICS_IP HMW ICS_IP_RES HMW HMW HMW SC SC HMW ICS_F ICS_F HMW HMW, SC SC

HMW HMW HMW HMW HMW, ICS_IP HMW HMW ICS_IP ICS_IP HMW, ICS_IP SC HMW

HMW HMW HMW ICS_IP HMW HMW


917

Table 1. Continued no. 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 918

ion name imidacloprid indoxacarb ioxynil iprodione iprovalicarb isazofos isocarbophos isofenphos isofenphos-methyl isofenphos-oxon isoprocarb isoproturon isoxaflutole isoxathion ivermectin a ivermectin b kresoxim-methyl lenacil linuron lufenuron malaoxon malathion mebendazole mecarbam mepanipyrim merphos metalaxyl metamitron metazachlor metconazole methamidophos methidathion methiocarb methiocarb sulfone methiocarb sulfoxide methomyl methoxyfenozide metobromuron metolachlor metolcarb metosulam metoxuron metribuzin metsulfuron methyl mevinphos miconazole molinate monocrotophos monolinuron monuron myclobutanil naled napropamide neburon nitenpyram nuarimol ofurace omethoate oxadixyl oxamyl oxfendazole oxycarboxin paclobutrazol paraoxon ethyl parathion ethyl pebulate penconazole pendimethalin phorate phorate sulfone phosalone phosmet phosphamidon picoxystrobin

RT (min) 6.45 11.79 9.45 10.65 9.91 11.03 9.60 12 11.63 9.74 9.10 8.76 10.07 11.94 15.68 14.78 11.13 7.95 9.95 12.29 8.11 10.70 7.47 11.06 10.41 8.1 8.79 6.08 9.21 10.60 1.47 9.98 9.67 6.93 5.91 5.14 10.4 9.17 10.87 7.75 8.74 7.37 8.18 8.03 6.73 9.02 10.51 5.07 8.92 7.76 10.01 9.18 10.38 10.98 7.83 9.23 8.90 2.19 7.71 4.52 6.42 7.21 9.46 8.99 11.32 12.03 10.61 13.01 11.88 9.6 11.83 9.95 7.3 12.02

m/z calcd 256.0596 528.0780 371.8377 330.0407 321.2173 314.0490 312.0430 346.1236 332.1079 330.1464 194.1176 207.1492 360.0512 314.0610 897.4971 883.4814 314.1387 235.1441 249.0192 510.9857 315.0662 331.0433 296.1030 330.0593 224.1182 299.1085 280.1543 203.0927 278.1055 320.1524 142.0086 324.9511 226.0896 258.0795 242.0845 185.0355 369.2173 259.0077 284.1412 188.0681 418.0138 229.0738 215.0961 382.0816 225.0523 414.9933 188.1104 224.0682 215.0582 199.0633 289.1215 378.7898 272.1645 275.0712 271.0956 315.0695 282.0891 214.0297 279.1339 242.0570 316.0750 268.0638 294.1368 276.0631 292.0403 204.1417 284.0716 282.1448 283.0020 293.0099 367.9941 318.0018 300.0762 368.1104


ion [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +


H] H]+ H]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+

C9H11ClN5O2 C22H18ClF3N3O7 C7H4I2NO C13H14Cl2N3O3 C18H29N2O3 C9H18ClN3O3PS C11H16NO4PSNa C15H25NO4PS C14H23NO4PS C15H25NO5P C11H16NO2 C12H19N2O C15H13F3NO4S C13H17NO4PS C48H74NaO14 C47H72NaO14 C18H20NO4 C13H19N2O2 C9H11Cl2N2O2 C17H9Cl2F8N2O3 C10H20O7PS C10H20O6PS2 C16H14N3O3 C10H21NO5PS2 C14H14N3 C12H28PS3 C15H22NO4 C10H11N4O C14H17ClN3O C17H23ClN3O C2H9NO2PS C6H11N2O4PS3Na C11H16NO2S C11H16NO4S C11H16NO3S C5H10N2O2SNa C22H29N2O3 C9H12BrN2O2 C15H23ClNO2 C9H11NO2Na C14H14Cl2N5O4S C10H14ClN2O2 C8H15N4OS C14H16N5O6S C7H14O6P C18H15Cl4N2O C9H18NOS C7H15NO5P C9H12ClN2O2 C9H12ClN2O C15H18ClN4 C4H8Br2Cl2O4P C17H22NO2 C12H17Cl2N2O C11H16ClN4O2 C17H13ClFN2O C14H17ClNO3 C5H13NO4PS C14H19N2O4 C7H13N3O3SNa C15H14N3O3S C12H14NO4S C15H21ClN3O C10H15NO6P C10H15NO5PS C10H22NOS C13H16Cl2N3 C13H20N3O4 C7H17O2NaPS3 C7H18O4PS3 C12H16ClNO4PS2 C11H13NO4PS2 C10H20ClNO5P C18H17F3NO4

commentsa HMW HMW ICS_IP HMW ICS_IP_RES HMW, ICS_IP HMW HMW ICS_IP ICS_IP HMW SC SC

ICS_IP

HMW ICS_IP_F HMW HMW HMW

ICS_IP_F

HMW HMW

Table 1. Continued no. 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297

ion name pirimicarb pirimiphos-methyl prochloraz procymidone promecarb prometryn propachlor propamocarb propanil propaphos propargite propazine propham propiconazole propoxur prosulfocarb prothiophos pyrazophos pyridaben pyridalyl pyridaphenthion pyridate pyrimethanil pyrimidifen pyriproxyfen quinalphos quinomethionate quinoxyfen quizalofop-ethyl rotenone silafluofen simazine simetryn spinosyn a spinosyn d spiromesifen spiroxamine sulfosulfuron sulfotep sulprofos tebuconazole tebufenozide tebufenpyrad teflubenzuron terbufos terbumeton terbuthylazine terbutryn tetrachlorvinphos tetraconazole thiabendazole thiacloprid thiamethoxam thifensulfuron-methyl thiocyclam hidrogenoxalate thiodicarb thiophanate-ethyl thiosultap tolfenpyrad tolylfluanid triadimefon triadimenol triasulfuron triazophos tribenuron-methyl trichlorfon triclocarban tricyclazole trifloxystrobin triflumizol triflumuron trifluralin triphenyl phosphate vamidothion vinclozolin xmc

RT (min) 5.67 9.77 9.45 10.79 10.04 8.26 9.34 2.23 9.66 10.74 12.97 9.61 9.34 10.88 8.34 12.47 10.77 11.52 13.6 14.96 10.18 14.33 8.23 10.30 12.72 11.34 12.12 12.26 12.13 10.70 14.49 7.7 6.62 9.06 9.29 13.35 8.28 9.05 11.78 12.85 10.34 10.91 12.24 9.89 12.67 6.78 9.75 8.36 10.64 10.14 4.71 7.34 5.68 7.83 1.83 8.06 9.20 1 12.24 11.72 10.15 9.46 8.25 10.73 9.25 5.76 11.68 9.68 11.95 10.50 11.22 12.74 11.48 5.82 11.13 1.85

m/z calcd 239.1503 306.1036 376.0381 284.0240 208.1332 242.1434 212.0837 189.1598 218.0134 305.0971 373.1444 230.1166 202.0838 342.0771 210.1125 252.1417 344.9701 374.0934 365.1449 489.9753 341.0719 379.1241 200.1182 378.1943 322.1438 299.0613 234.9994 308.0040 373.0950 395.1489 431.1813 202.0853 214.1121 732.4681 746.4838 371.2217 298.2741 471.0751 323.0305 323.0358 308.1524 353.2224 334.1681 380.9815 289.0514 226.1662 230.1166 242.1434 364.9065 372.0288 202.0433 253.0309 292.0266 388.0380 182.0126 377.0382 371.0842 311.9699 384.1473 368.9671 294.1004 296.1160 402.0633 314.0723 396.0972 256.9298 314.9853 190.0433 409.1369 346.0929 359.0405 336.1166 327.0781 288.0488 286.0032 180.1019

ion [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M [M

H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ Na]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ H]+ +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + H]

elemental compositions C11H19N4O2 C11H21N3O3PS C15H17Cl3N3O2 C13H12Cl2NO2 C12H18NO2 C10H20N5S C11H15ClNO C9H21N2O2 C9H10NOCl2 C13H22O4PS C19H26O4NaS C9H17ClN5 C10H13NO2Na C15H18Cl2N3O2 C11H16NO3 C14H22NOS C11H16Cl2O2PS2 C14H21N3O5PS C19H26ClN2OS C18H15Cl4F3NO3 C14H18N2O4PS C19H24ClN2O2S C12H14N3 C20H29ClN3O2 C20H20NO3 C12H16N2O3PS C10H7N2OS2 C15H9Cl2FNO C19H18ClN2O4 C23H23O6 C25H30FO2Si C7H13ClN5 C8H16N5S C41H66NO10 C42H68NO10 C23H31O4 C18H36NO2 C16H19N6O7S2 C8H21O5P2S2 C12H20O2PS3 C16H23ClN3O C22H29N2O2 C18H25ClN3O C14H7Cl2F4N2O2 C9H22O2PS3 C10H20N5O C9H17ClN5 C10H20N5S C10H10Cl4O4P C13H12Cl2F4N3O C10H8N3S C10H10ClN4S C8H11ClN5O3S C12H14N5O6S2 C5H12NS3 C10H18N4O4S3Na C14H19N4O4S2 C5H14NO6S4 C21H23ClN3O2 C10H13Cl2FN2O2S2Na C14H17ClN3O2 C14H19ClN3O2 C14H17ClN5O5S C12H17N3O3PS C15H18N5O6S C4H9Cl3O4P C13H10Cl3N2O C9H8N3S C20H20F3N2O4 C15H16ClF3N3O C15H11ClF3N2O3 C13H17F3N3O4 C18H16O4P C8H19NO4PS2 C12H10Cl2NO3 C10H14NO2

commentsa HMW ICS_IP_F ICS_F ICS_F

HMW ICS_F, SC HMW, SC HMW HMW HMW HMW HMW HMW SC HMW

HMW HMW, ICS_RES HMW ICS_F SC HMW HMW HMW HMW, ICS_F

HMW HMW HMW ICS_F, SC ICS_F HMW HMW

HMW HMW HMW HMW, ICS_IP HMW HMW ICS_IP HMW

HMW HMW, ICS_IP HMW, ICS_IP HMW

a Comments (for details, see text): ICS, isobaric coeluting species; HMW, relative high molecular weight compound; SC, sensitive compound; ICS_F, isobaric coeluting species resolved by characteristic fragmentation; ICS_IP, isobaric coeluting species resolved by characteristic isotopic pattern; ICS_RES, isobaric coeluting species distinguished by instrument resolution on the m/z axis; ICS_IP_F, isobaric coeluting species resolved by characteristic fragmentation and isotopic pattern.

LC-TOFMS screening method based on the created accuratemass database. For confirmation and comparison purposes, the same samples were analyzed by LC-MS/MS in the MRM mode, using a 6410 Triple Quad LC-MS (Agilent Technologies, Santa Clara, CA) equipped with an electrospray source in positive ionization mode. The method used for comparison purposes

includes 150 compounds of those available in the database, and it is described in detail by Kmella´r et al.10

(24) Martıńez-Bueno, M. J.; Agu ¨ era, A.; Go´mez, M. J.; Hernando, M. D.; GarcıáReyes, J. F.; Fernańdez-Alba, A. R. Anal. Chem. 2007, 79, 9372–9384.


919

RESULTS AND DISCUSSION General Considerations on the Development of AccurateMass Databases of Pesticides in Complex Matrixes by LC-TOFMS. Table 1 show the data of the studied compounds included in the database. The detailed theoretical masses of the protonated molecule (or sodium adducts) together with the masses of the main fragment ions and isotope signature ions are included in Table S1 (Supporting Information). The investigated pesticides are listed in alphabetical order followed by their fragments named with the pesticide name and followed by F1, F2, F3,..., depending the number of the fragments per pesticide. Seventy-five percent of the pesticides studied contain at least one fragment ion. Figure S1 (Supporting Information) shows the appearance of a user-friendly Access database created when collecting all the experimental data of the studied compounds. This access database has been created to search information by different search parameters (mass search, pesticide search, retention time search or formula search). To perform a search, the value (mass, name, or retention time) is inserted, and by clicking the desired button, the database yields the demanded information. The default tolerances in this database are ±0.15 min if the search is by retention time and ±0.03 Da if the search is performed by exact mass. Concerning the LC-MS method development, default instrument parameters were selected when optimizing the method. Electrospray ionization in the positive ion mode was selected since most of the pesticides work better in positive ionization mode. Future research and the improvement of instruments would enable collecting the data with both negative and positive ionization mode. Another issue to consider is the sample treatment protocol used. This step is somehow a bottleneck for the development of universal screening procedure. Limitations on the sample treatment method will affect the screening procedure. Conflictive compounds not recovered by generic sample treatment step (i.e., chlormequat, paraquat) might not be sought with the screening method. In these cases, single-residue methods would be required. We selected the QuEChERS extraction protocol as default sample treatment for the screening procedure. So far, this method has been used successfully for a large variety of compounds obtaining recovery rates close to 100%.22,23 However, some compounds might not be quantitatively recovered. The sensitivity of the screening method is also a key feature to assess the viability of the screening procedure. LC-TOFMS instruments offer sensitivity and detection limits at the low picogram range as described elsewhere.3,4 In this work, the limits of detection (LODs) of the 297 pesticides included in the database were calculated by the injection of a matrix-matched solution at different concentration levels: 5, 10, and 50 µg kg-1. Since this screening method is intended to be used in a wide range of food matrixes, a dedicated study of LODs was not made. As a model matrix we used lettuce, although from our experience so far, in the particular case of fruits and vegetables, in most of the matrixes tested with QuEChERS, the LODs are not strongly affected by the matrix. The pesticides included in the database were sorted in four groups according to their sensitivity and detection capabilities: lower or equal than 5 µg kg-1, higher than 5 µg kg-1 and lower than 10 µg kg-1, higher 920


Table 2. Accurate Mass Measurement of Selected Pesticides in a Lemon Fruit Extract Spiked at 50 µg kg-1 pesticide

accurate mass (exptl)

theor mass

error (ppm)

acetamiprid atrazine azoxystrobin buprofezin carbendazim dimethoate dimethomorph diuron imazalil imidacloprid isoproturon lufenuron pyriproxyfen prochloraz

223.0744 216.1006 404.1243 306.1635 192.0764 230.0070 388.1313 233.0240 297.0557 256.0593 207.1487 510.9861 322.1442 376.0376

223.0745 216.1014 404.12409 306.16346 192.07675 230.0069 388.13101 233.02429 297.05559 256.05957 207.14918 510.98570 322.14377 376.03808

0.5 2.0 0.5 0.1 1.8 0.4 0.7 1.3 0.4 1.1 2.4 0.8 1.3 1.3

than 10 µg kg-1 but lower or equal to 50 µg kg-1, and a last group which includes pesticides with an LOD higher than 50 µg kg-1. Twenty percent of the pesticides have LODs lower or equal to 5 µg kg-1, 40% are included in the second group with LODs between 5 and 10 µg kg-1, 20% have LODs between 10 and 50 µg kg-1, and only 20% have LODs higher than 50 µg kg-1. The sensitivity attained using this screening method is enough for most of the combinations of pesticide/commodities. It should be emphasized that we used a relatively a high fragmentor voltage, aiming at obtaining additional information of fragment ions for confirmation purposes. The main drawback is a loss of sensitivity on the compounds, since the intensity of the [M + H]+ decreases as fragment ions are formed. As a balance between sensitivity and fragmentation, the voltage at the exit of the capillary (fragmentor voltage) was found to be optimum at 190 V. In this sense, continuous upgrades and improvements are being introduced by different manufacturers related with enhanced electronics which increase both the sensitivity and selectivity (increased mass resolution) and also with the use of improved ion sources so that LODs might be significantly improved by using a state-of-the-art TOF instrument. Another subject that must be considered when performing screening methods based on accurate-mass databases is the mass accuracy. The reliability of this screening method depends heavily on the ruggedness of the TOF instrument in order to provide consistent accurate mass measurements within a fixed mass error tolerance, typically as low as 3 ppm. From the data collected throughout the study and from experience with the instrument in methods for the identification and quantitation of trace amounts of pesticides in different complex matrixes,3,16,24 accurate mass measurements are below 2 ppm in most cases, regardless of the matrixes or the concentration level. As an example, accurate mass measurements of selected pesticides in a spiked fruit extract are shown in Table 2. Only large concentrations or very sensitive compounds may yield higher errors due to the saturation of the detector. Design of the Accurate-Mass Database: Selectivity Provided by Retention Time and Fragmentation Data. The comprehensive screening of various hundreds of pesticides in complex matrixes in a unique LC run of a few minutes is a demanding task. We must consider also that most pesticides behave in a very similar fashion under conventional reversed-phase

Figure 1. Example of an LC-MS/MS run for the analysis of 148 pesticides using a hybrid triple quadrupole linear ion trap in the multiple reaction monitoring (MRM) mode. Most of the compounds are eluted in the same region of the chromatogram, showing the complexity of performing an MRM method for such a large amount of compounds in a unique LC run.

columns. Figure 1 shows the appearance of the overlapped MRM transitions for the analysis of about 150 pesticides in vegetable samples. Note that most of the pesticides elute in the region in the range from 60% to 90% acetonitrile/methanol composition of mobile phase. For instance, a method for 300 pesticides would need up to 900 MRM transitions, if we fix three product ions for unambiguous confirmation. Even using several segment windows, problems arising due to interferences from isobaric coeluting species with any common transitions are very likely.10 Therefore, it is essential to gain specificity when developing a screening method based on accurate-mass databases. The m/z value itself is not enough to obtain reliable screening results. From the pesticides studied, we found 113 nonisobaric species and 184 isobaric species. We used the term isobaric species, that is “two or more species with the same nominal mass value, so that they cannot be distinguished by low-resolution mass spectrometry”. In this sense, it is obvious the possibility of encountering isobaric species increases as we extend the number of compounds included in the database. Most of these compounds are resolved by retention time so that demonstrates the need of providing retention time data, when developing large-scale databases for automated screening, regardless of the field of application. Besides, when analyzing real samples, a greater number of potential interfering species might be expected, and the retention time can be used to get rid from most of them, when performing screening of samples. Therefore, the addition of retention time provides a higher degree of specificity, mandatory for this kind of application. Interestingly, another advantage of using retention time data relies on the way the search is performed. If retention data is included in the database, the search for target species is performed on a reduced section of the raw LC-MS data so that the search is faster and the throughput increases as well. The time devoted to perform the search of a sample was in the range of 1-4 min depending on the complexity of the matrix. Note this is approximately the time for reconditioning the chromatographic

system for the subsequent run. Without retention time, this step would be more time-consuming and would become a bottleneck in the screening procedure. Table 3 shows the data of isobaric coeluting species. Twentyseven pairs of compounds were somehow overlapped. The rest of the isobaric compounds were resolved by significant retention time differences, which highlights the importance of using a consistent and dedicated chromatographic method. From our experience, the chromatography for these large-scale screening methods should not involve a gradient shorter than 30 min with a conventional HPLC system or 10 min with small-particle-sizebased HPLC systems. We strongly discourage “ultrashort” methods (i.e., less than 7 min), since performance might be strongly affected by matrix (i.e., matrix-induced signal suppression,...) and the possibility of finding interferences due to other target species or simply due to unknown interferences from the matrix increases as the total run time is reduced. As can be seen in Table 3, it is not difficult to find out isobaric coeluting species. For these cases, neither nominal m/z values nor retention time would itself be enough for performing reliable screening of samples. At this point, the resolution of the instrument on the m/z axis could be used to distinguish between some of the isobaric coeluting species. In this sense, advances in instrumentation recently introduced, i.e., the use of a TOF with a 2.5 m flight tube and enhanced electronics, are supposed to provide a resolution of 30 000-50 000 for small-molecule research.25 Besides, another instrument that has been introduced recently with the same profile of TOF in terms of full-scan acquisition capabilities with high sensitivity and high resolution is the new nonhybrid “exactive” Orbitrap analyzer, which is supposed to provide a resolution >50 000 (25) Owens, B.; Decker, P.; Krebs, I. Comprehensive multi-target screening of pesticides in food extracts using HPLC-ESI-TOFMS. Presented at the 56th ASMS Conference, Denver, CO, 2008; Poster ThP 146.


921

Table 3. Summary of Isobaric Coeluting Species (ICS) Occurring in the Pesticide Database group

RTa

theor exact mass

0.1 Da > ICS > 0.01 Dab

1

1.90 1.47 9.68 9.61 8.77 8.79 8.26 8.23 9.21 9.34 6.73 6.69 9.67 9.88 9.41 9.75 9.61 8.26 8.36 9.95 9.75 10.94 10.90 10.72 10.50 9.72 9.46 9.46 9.46 11.67 11.73 10.25 10.49 10.50 10.27 9.23 9.36 9.54 9.74 10.02 9.92 10.65 11.63 11.18 11.08 10.37 10.65 11.08 11.22 8.43 8.41

142.0086 142.0086 190.0433 190.0667 192.1383 192.1383 200.0964 200.1182 212.0650 212.0837 225.0523 225.0715 226.0896 226.1074 226.1339 230.1166 230.1166 242.1434 242.1434 249.0192 249.0470 270.1255 270.1255 278.0247 278.0554 294.1181 294.1368 296.1160 296.1338 305.0168 305.1083 310.9944 311.0393 316.0791 316.1076 317.0665 317.1642 330.1100 330.1464 330.9455 331.0399 332.0377 332.1079 332.9696 332.9789 339.1185 339.1703 358.9768 359.0405 360.0341 360.0373

acephate F1 methamidophos tricyclazole propazine F1 Cl-37 deet metalaxyl F3 prometryn F1 pyrimethanil metazachlor F2 Cl-37 propachlor mevinphos acetamiprid Cl-37 methiocarb diethofencarb F1 cyprodinil terbuthylazine propazine prometryn terbutryn linuron fludioxonil acetochlor alachlor fenitrothion triflumizol F1 cyproconazole Cl-37 paclobutrazol triadimenol paclobutrazol Cl-37 clofentezine Cl-37 diazinon fluquinconazole F1 Cl-37 diflubenzuron hexaconazole Cl-37 flusilazole nuarimol Cl-37 bupirimate fluridone isofenphos-oxon dimethylvinphos fenarimol iprodione Cl-37 isofenphos-methyl dichlofluanid chlorfenvinphos F1 Cl-37 fenbuconazole Cl-37 chromafenozide F1 chlorfenvinphos triflumuron chlorsulfuron Cl-37 florasulam

12.05 12.03 10.14 10.11

364.0760 364.0946 372.0288 372.0979

fenoxaprop-ethyl Cl-37 benzoximate tetraconazole azoxystrobin F1

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 a

0.01 Da > ICS > 0.001 Dab acephate F1 methamidophos deet metalaxyl F3

comments distinguished by retention time tricyclazone does not have characteristic fragment ions; distinguished by resolving powerc distinguished by characteristic fragmentation pyrimethanil cannot be distinguished when prometryn is present; distinguished by resolving powerc distinguished by retention time and/or resolving power distinguished by characteristic fragmentation distinguished by characteristic fragmentation, retention time, and/or resolving power

terbuthylazine propazine prometryn terbutryn

distinguished by characteristic fragmentation distinguished by characteristic fragmentation distinguished by characteristic fragmentation, retention time, and/or resolving power distinguished by characteristic fragmentation

acetochlor alachlor

resolved by retention time and/or resolving power distinguished by retention time and/or resolving power distinguished by characteristic fragmentation distinguished by resolving power distinguished by retention time and/or resolving power distinguished by retention time and/or resolving power distinguished by retention time and/or resolving power distinguished by retention time and/or resolving power distinguished by resolving power distinguished by retention time and/or resolving power

dichlofluanid distinguished by characteristic fragmentation chlorfenvinphos F1 Cl-37 distinguished by retention time and/or resolving power distinguished by resolving power chlorsulfuron Cl-37 florasulam

florasulam cannot be distinguished when chlorsulfuron is present; cannot be distinguished by characteristic fragmentation distinguished by resolving power distinguished by retention time and/or resolving power

RT: retention time. b ICS: isobaric coeluting species. c Distinguished by resolving power depending on the MS instrument resolution.

with scan speeds fitting HPLC separation (i.e., 0.5-1 s/scan).26,27 These features would fit smartly for the approach presented here too. The TOF instrument we used has a limited resolution of ca. 8000, which can resolve interferences in the range of 30 mDa (for the range of masses common to pesticides) so that it could be used for resolving most of the cases included in Table 3. However, some of the isobaric species has exact mass differences in some cases lesser than 20-30 mDa which is the mass difference that can be discriminated by resolution of the analyzer on the m/z axis. Some of the isobaric species are isomers with the same elemental composition and accurate

mass. Therefore, additional information included in the database must be employed to differentiate between these isobaric species, when retention time is not enough. For this reason, we included information of fragment ions from in-source CID fragmentation if available. Over 400 fragment ions were included in the database (see Table S1, Supporting Information). Over 75% of the pesticides contained at least one characteristic fragment ion. The use of fragmentation information can be used not only to differentiate with high-resolution isobaric compounds and isomers but also as a complementary set of data to provide unambiguous confirmation of the findings.

(26) Bateman, K.; Muenster, H.; Kellmann, M.; Tiller, P.; Papp, R.; Taylor, L. C. Full-scan data acquisition for rapid quantitative and qualitative analysis using a benchtop non-hybrid ESI-Orbitrap mass spectrometer. Presented at the 56th ASMS Conference, Denver, CO, 2008; Presentation ThOF-3.

(27) Kellmann, M.; Taylor, L. C.; Ghosh, D.; Wieghaus, A.; Muenster, H. High resolution and high mass accuracy: a perfect team for food and feed analysis in complex matrices. Presented at the 56th ASMS Conference, Denver, CO, 2008; Poster ThP 133.

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Another tool that can be used to distinguish between two isobaric species is the isotopic profile due to chlorine, bromine, and sulfur. The created database includes retention time, fragments of most compounds, and the signal (theoretical exact mass) of 37Cl, 81Br, and 34S for chlorinated, brominated, and sulfurated pesticides and its fragments. The database contains 115 pesticides with chlorine atoms, 8 pesticides with bromine atoms, and 92 species with sulfur atoms (Table S1, Supporting Information). The screening method used, together with the protonated molecules, both isotopic signals and fragment ions as different species but with the same retention time of the compound so that both monoisotopic masses should be found to provide unambiguous confirmation. In fact, A + 2 isotope signals can be a very useful for identification purposes. However, we should bear in mind that the intensity/relative abundance of some of the isotopic signatures are low and, therefore, might not be useful for confirmatory purposes when dealing with positive samples containing the target species at concentration levels approaching the LOD. In this situation, the identification/confirmation using a “low abundant” isotopic signal might fail because either the concentration is so low that the A + 2 signal cannot be detected (i.e., the relative contribution of sulfur 34S is 4.22%) or there is any background interference that is overlapped with our target, thus giving poor accurate mass measurements. In these cases working with low pesticide concentration, it is not possible to consider these low-abundant isotopic signatures as a convenient identification criterion. Recent advances on a new version of the same software include the isotopic signature profile information so that the searching confidence taking into account the isotopic profile of the extracted “features” increases with regards to the monoisotopic search shown here. To illustrate the usefulness of the fragmentation data included in the database, some examples are described in detail. Figure 2a shows the mass spectra of two pesticides, linuron and fludioxonil, which, depending on the chromatographic method, might not be separated. Linuron mass spectra show the [M + H]+ and 37Cl signal at m/z 249.0199 and 251.0161, respectively. Fludioxonil mass spectra show the [M + H]+ at m/z 249.0472. The exact mass difference is about 28 mDa, which, depending on the instrument resolution, might be enough to differentiate both compounds with the resolution of the instrument on the m/z axis. Besides, the linuron molecule contains two chlorine atoms, which is enough to confirm its presence in a mixture with fludioxonil. However, we could not confirm the presence of fludioxonil in a mixture with linuron, unless it possesses characteristic fragment ions, which could assist its unambiguous confirmation. Effectively, the ESI-TOFMS mass spectrum of fludioxonil shown in Figure 2a, reveals as base peak the fragment ion m/z 229.0415 [C12H7FN2O2]+ which gives evidence of fludioxonil in the presence of linuron, illustrating the utility of including fragmentation data in the database. In addition, linuron mass spectrum shows two fragment ions at m/z 182.0245 and m/z 159.9719 which correspond with [C8H7ClN2O]+ and [C6H4Cl2N]+, respectively, which could be used for confirmatory purposes in a complex matrix.

A few groups of isobaric compounds, shown in Table 3, are not fully resolved depending on the chromatographic method. In these cases, the combined use of accurate mass measurements (if resolution is enough), isotopic signature information, along with characteristic fragmentation of each pesticide provides the unambiguous identification of each compound in the coeluting mixture. These examples are marked in Table 1 (comments column) as isobaric coeluting species which are resoluble because they have different isotopic profiles and/or characteristic fragmentation. Figure 2b shows the mass spectra of alachlor and acetochlor as an example of two isomers that are compounds with the same elemental composition and accurate mass (m/z 270.1255). Besides, the retention time (10.94 min for acetochlor and 10.90 min for alachlor) could not be used. In this case, they cannot be differentiated by isotopic profile, since both of them have a chlorine atom. However, they can be perfectly distinguished because they have very different fragmentation: acetochlor fragments in-source to yield ions at m/z 224 [C12H15ClNO]+ and 148 [C10H14N]+, whereas alachlor characteristic fragments are m/z 238 [C13H18ClNO]+ and m/z 162 [C11H16N]+ as main fragments. This is another example of how essential is the fragmentation information for this application. Data shown in Table 3 indicate that the combination of retention time, accurate mass/high resolution, isotopic signature, and characteristic fragmentation was found to be successful to distinguish as many as 99% of the 297 compounds included in the database. The only isobaric coeluting species that could not be resolved were pyrimethanil in the presence of prometryn and florasulam in the presence of chlorsulfuron. For these two compounds, the proposed approach was not found to be selective enough. Development and Optimization of the Automated Method for Large-Scale Screening of Pesticides. We used the software available in the instrument described in the Experimental Section. This Agilent molecular feature extractor (MFE) is a standard part of the MassHunter Qualitative Analysis software. The automated screening method consisted of two steps: (a) extraction of the compounds (MFE) from the raw data (fullscan TOFMS positive ion mode spectra); (b) database search. Both steps should be carefully examined, and parameters affecting the performance must be tuned according to the application, which involves the detection of compounds at concentration levels which can differ up to 4 orders of magnitude (we may search for compounds at concentration levels as low as 0.5 ng g-1 but also have to detect compounds at 5 µg g-1). Compound Extraction from the Raw Data. The MFE software examines the whole chromatogram at once in order to group m/z values that are logically related and maximized at same retention time. Molecular features are discrete molecular entities defined by a particular retention time (RT) and m/z value. These features that are pulled out from the chromatographic and mass spectrometric raw data can be compounds of interest but also adducts or fragments ions corresponding either to target compounds included in the database list or to compounds from the matrix. This means that all the compounds present in a data are characterized. However, due to the complexity of the sample, some filters should be applied to reduce the total number of Analytical Chemistry, Vol. 81, No. 3, February 1, 2009

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Figure 2. (a) LC-electrospray(+)-TOFMS mass spectra of linuron (a.1) (m/z 249.0191) and fludioxonil (a.2) (m/z 249.0472) showing the importance of fragmentation data to confirm the presence of each pesticide; (b) LC-electrospray(+)-TOFMS mass spectra of acetochlor (b.1) ([M + H]+ m/z 270.1255) and alachlor (b.2) ([M + H]+ m/z 270.1255) showing the role of in-source fragmentation (m/z 148.1121 (acetochlor F2) and m/z 162.1277 (alachlor F1)) to confirm the presence of each pesticide. For details, see text.

compounds extracted (to increase the high throughput of the procedure). For this reason, the extraction of the compounds from the raw data involves two filtering steps. The first filter is applied directly on the mass spectrum data. The goal is to remove “persistent” chemical background. A minimum number of counts is also set (typically 100-200 counts) just to get rid of background or chemical noise. The MFE software maps signals in the 3D in time and mass at the mass spectrum levels, removing data corresponding to noise. After the extraction of compounds is performed, we 924


may apply additional filtering criteria which now apply not to the mass spectrum but to extracted ion chromatograms on selected time windows (to all the peaks/compounds extracted). These filters are referred to the main chromatographic peak (minimum relative height (gpercent related to the base peak of the extracted ion chromatogram) or just by absolute counts value (absolute relative height (gx counts)). The number of molecular features detected in this step depends strongly on the complexity of the matrix tested and also on the defined peak and compound filtering settings. We selected

Figure 3. Screening of pesticide residues in a tangerine sample (s59) by LC-TOFMS: (a) total ion chromatogram; (b) extracted ion chromatograms of buprofezin (m/z 306.1634 ( 5 mDa) and pyriproxyfen (m/z 322.1438 ( 5 mDa); (c) accurate mass spectra of buprofezin (RT, 11.39 min) and pyriproxyfen (RT, 12.65 min).

a peak filter of g100 counts and the compound filter was relative abundance higher or equal to 0.01% and absolute abundance higher or equal to height of 1000 counts. These values provide 100% positives on spiked samples tested, keeping reduced the total

number of features extracted. With these settings, the number of features extracted in real fruit and vegetable samples depended on the matrix complexity and were in the range of 2000-8000/ sample. Note that the number of features does not correspond to Analytical Chemistry, Vol. 81, No. 3, February 1, 2009

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Figure 4. Screening of pesticide residues in an apple sample (s18) by LC-TOFMS: (a) total ion chromatogram; (b) extracted ion chromatograms of acetamiprid (m/z 223.0745 ( 5 mDa), ethoxyquin (m/z 218.1539 ( 5 mDa), imazalil (m/z 297.0556 ( 5 mDa), iprodione (m/z 330.0407 ( 5 mDa), and thiabendazole (m/z 202.0433 ( 5 mDa); (c) accurate mass spectra of acetamiprid (RT, 6.69 min), ethoxyquin (RT, 8.32 min), imazalil (RT, 7.43 min), iprodione (RT, 10.67 min), and thiabendazole (RT, 4.70 min). 926


Table 4. Analysis of 60 Market-Purchased Fruit and Vegetable Samples Using the Proposed Automatic Screening Method (LC-TOFMS) and a Multiresidue Method for the Analysis of 150 Pesticides by LC-MS/MS (ref 10) Used for Comparison Purposesa sample

LC-TOFMS findings

LC-MS/MS findings (µg kg-1)

red apple s1

thiabendazole imazalil carbendazim*

green pepper s2

myclobutanil pyrimethanil imidacloprid methoxyfenozide pyrimethanil spinosyn A thiamethoxam imidacloprid* pirimifos met azoxystrobin fenhexamid azoxystrobin dimethoate acetamiprid metalaxyl dimetomorph thiabendazole imazalil pirimicarb dichlorvos thiabendazole imazalil metoxifenocide imazalil buprofezin iprodione ethoxyquin imazalil fenoxycarb fuoroacetamide dichlorvos ethoxyquin thiabendazole imazalil myclobutanil metoxifenocide azoxystrobin tebuconazole cyprodinil

thiabendazole (12.0) imazalil (1.8) carbendazim (1.0) metoxifenocide (0.5) myclobutanil (8.6) pyrimethanil (2.1) imidacloprid (10.0) methoxyfenozide (3) pyrimethanil (8.7) spinosyn A (5.3) thiamethoxam (54.3) imidacloprid (20.0) pirimifos met (15.0) azoxystrobin (13.0) fenhexamid (2.2) azoxystrobin (20.7) dimethoate (20.0) acetamiprid (10.9) metalaxyl (2.9) dimetomorph (15.0) thiabendazole (13.6) imazalil (4.89) pirimicarb (1.4) dichlorvos (12.32) thiabendazole (23.9) imazalil (10.1) metoxifenocide (1.0) imazalil (20.9) buprofezin (0.9) iprodione (30.1) not analyzed imazalil (15.7) fenoxycarb (5.9) not analyzed dichlorvos (13.0) not analyzed thiabendazole (20.0) imazalil (10.2) myclobutanil (1.0) metoxifenocide (1.2) azoxystrobin (23.4) tebuconazole (50.1) cyprodinil (0.9)

acetamiprid triadimenol*

acetamiprid (4.3) triadimenol (27.0)

imidacloprid acetamiprid ethoxyquin imazalil iprodione thiabendazole bupirimate fenhexamide propiconazole cambendazole

imidacloprid (21.1) acetamiprid (8.3) not analyzed imazalil (13.4) iprodione (32.8) thiabendazole (24.1) bupririmate (10.2) fenhexamide (7.9) propiconazole (2.0) cambendazole (5.9)

cyprodinil

cyprodinil (2.5)

diazinon tebuconazole

diazinon (1.0) tebuconazole (21.8)

cyprodinil kresoxim methyl fludioxonil fenarimol azoxystrobin triadimenol

cyprodinil (3.2) kresoxim methyl (12.7) fludioxonil (60.3) fenarimol (23.2) azoxystrobin (21.8) triadimenol (25.9)

red pepper s3

pepper s4 kiwi s5 lettuce s6

apple s7

apple s8 pear s9 pear s10

pear s11

pepper s12 pepper s13 tomato s14 pepper s15 pepper s16 lettuce s17 apple s18

strawberry s19 cucumber s20 red pepper s21 tomato s22 tomato s23 pepper s24 pear s25 strawberry s26 strawberry s27 strawberry s28 strawberry s29 strawberry s30 a

sample

LC-TOFMS findings

LC-MS/MS findings (µg kg-1)

strawberry s31

strawberry s32 strawberry s33

cyprodinil

cyprodinil (6.7)

strawberry s34 strawberry s35 strawberry s36

fenarimol

fenarimol (3.1)

azoxystrobin

azoxystrobin (10.9)

strawberry s37

kresoxim methyl

kresoxim methyl (14.9)

strawberry s38

fenarimol

fenarimol (12.4)

strawberry s39

fenarimol

fenarimol (22.9)

imidacloprid

imidacloprid (40.6)

cyprodinil fludioxonil cyprodinil fludioxonil kresoxim methyl myclobutanil

cyprodinil (6.9) fludioxonil (43.0) cyprodinil (9.1) fludioxonil (60.5) kresoxim methyl (13.5) myclobutanil (5.2)

azoxystrobin triadimenol

azoxystrobin (12.7) triadimenol (21.8)

banana s49

imazalil

imazalil (12.3)

aubergine s50 tomato s51

imidacloprid pyriproxyfen buprofezin imazalil triadimenol cyproconazole imidacloprid

imidacloprid (13.0) pyriproxyfen (50.1) buprofezin (3.9) imazalil (15.9 triadimenol (21.3) cyproconazole (1.9) imidacloprid (12.0)

persimmon s56 tangerine s57

imazalil buprofezin pyriproxyfen

imazalil (21.2) buprofezin (4.1) pyriproxyfen (51.5)

pepper s58

azoxystrobin imidacloprid

azoxystrobin (11.4) imidacloprid (16.7)

strawberry s40

aubergine s41

strawberry s42 strawberry s43 strawberry s44 strawberry s45 strawberry s46 strawberry s47 tomato s48

tangerine s52 cucumber s53 red pepper s54 marrow s55

strawberry s59 lettuce s60

/ denotes measurement was detected manually.

the total number of chemical species, because both in-source fragment ions and isotopic signals are considered as features. Therefore, considering for instance three isotopic signals per compound and a fragment ion per compound as average, the number of compounds is ca. 300-1000 depending on each individual matrix. A snapshot of the software output is shown in

Figure S2 (Supporting Information). It includes the RT and m/z value of the extracted features, and those features which matched with any of the target species included in the database, according to the database search parameters selected. Database Search. The second step is the database search. The molecular features or potential compounds of interest, Analytical Chemistry, Vol. 81, No. 3, February 1, 2009

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extracted from the raw data in the first step, are matched with the data of the target compounds included in the database. The data collected is included in a csv Excel file, and this file is matched with the compounds. In this step, the parameters that should be evaluated are accurate mass and retention time tolerances of the search, that is, the tolerance thresholds applied to the theoretical data, when compounds are searched. Different experiments were performed varying RT window tolerance between ±0.05 min and ±0.5. The use of large RT windows might yield false positives, whereas a small RT window could give rise to false negatives. Typically, with the column and gradient selected, the RT precision is very high ( 333 are highlighted in the database (noted in the database as “HMW” ) “high molecular weight compound”). Another issue is the broad mass range of species included in the database. Besides, the mass range of the target species covers a large range of m/z values, from m/z 70 (fragments) to, for instance, m/z 732-746 (spinosad), which is 10-fold. The 1 mDa involves a mass error of 1.3 ppm for spinosad. In contrast, in a m/z 70 fragment ion, it involves a mass error higher than 10 ppm. For this reason, it might be more convenient to establish mixed criteria of mass tolerance, based on both absolute and relative accurate-mass deviations, so that the m/z range differences are compensated. For this reason, in addition to the defined window of 1 and 10 mDa, an extra relative tolerance of 5 ppm (relative error) mass tolerance can be included. Therefore, we suggest two database searchings: (1) ±10 mDa (preliminary screening); (2) ±1 mDa and/or 5 ppm (confirmation), taking into account those compounds highlighted in the database because of the high sensitivity, relative high m/z value, or those which can be affected by potentially isobaric coeluting interferences. Evaluation of the LC-TOFMS Accurate-Mass Database Method for the Screening of Pesticide Residues in Fruit and Vegetable Samples. To evaluate the performance of the automated screening, first it was tested with ca. 40 different solutions containing selected groups of pesticides at different concentration levels and also with matrix-matched standards containing also some of the pesticides in the range 10-200 µg kg-1. The results obtained in these preliminary studies were satisfactory. After that, the proposed approach was applied to different marketpurchased samples which were also analyzed by an LC-MS/ MS method used as a reference, including 148 compounds.10 The results obtained are shown in Table 4. The proposed approach was evaluated for 60 different crop samples extracted by QuEChERS protocol. Seventy-five percent of the samples analyzed contained at least one pesticide (45 out of 60). In addition, up to six compounds were found in a sample. Both LC-TOFMS and LC-MS/MS gave the same results for 50 out of the 60 samples Only six compounds from 92 (6%) give not exactly the same results. The general reason of the disagreement is that all those positives findings were the different detection limits of both techniques. As an example the chromatograms of the positive compounds found in a tangerine sample (s57) and an apple (s18) are shown in Figures 3 and 4, respectively.

The samples and compounds which did not match by both techniques were as follows: methoxyfenozine and carbendazim (s1), imidacloprid (s3), ethoxyquin (s10, s11, s18), triadimenol (s14), and fluoroacetamide (s10). For instance, the concentration of methoxyfenozine in s1 is 0.5 µg kg-1 and the LC-TOFMS method is not sensitive enough to detect this concentration of this compound. Carbendazim (s1), imidacloprid (s3), and triadimenol (s14) were not detected automatically, although their presence was confirmed from the LC-TOFMS data manually. These were the unique examples in which the proposed approach was not successful. Finally, fluoroacetamide was not included in the LC-MS/MS method (s10) so that its finding could not be confirmed. These results show the potential of the proposed approach, with a high agreement with the LC-MS/MS conventional methodology on the common compounds. Although no false positives were found in the analyzed samples, the created database includes 25 pesticides which have no fragments and/or characteristic isotope profile (no atoms of chlorine, bromine, or sulfur in its structures). A low percentage of false positives could occur if a matrix component has the same exact mass of one of these 25 pesticides and the same retention time. In this sense, with regards with the selectivity, due to potential interferences of the matrix, from our experience it is quite unlikely to find out an interfering species with the same exact mass (i.e., within 0.025 Da) and retention time of the positive target analyte. In such case, most of the analytes contains “A + 2” signals, characteristic fragment ions, or both, so that we can overcome the problem due to this coeluting isobaric interference. In this study, we have already analyzed 100 samples with satisfactory results, barely detected cases where a severe interference (due to matrix ions) hampered the identification of the target pesticide in the studied sample. It is not common to find out interfering species with the same exact mass and RT (and also isotopic signals, fragmentation, etc.). To actually have true interference between any of the natural components of the matrix and a pesticide the following conditions must occur at the same time: (1) the analyte and the interfering species having the same retention time, (2) the analyte and the interfering species having similar accurate mass of the targeted ion (protonated molecule) (within 20-30 mDa), (3) the analyte and the interfering species must have the same isotopic profile or a similar enough one to hamper the identification, and (4), the in-source fragment ions of the analyte (if available) must also have the same exact mass of the interfering species. For these reasons the chance of finding interferences between a natural compound present in the sample and the studied pesticides is negligible. From the data collected in our laboratory on hundreds of fruit and vegetable samples, only a few of cases have been noticed in which the analyte signal was affected by the coelution of matrix components, and in these cases, either the use of in-source fragmentation or the isotopic signature was found effective to overcome these interferences. Improvements of instrument performance in sensitivity and/ or mass resolution are available and will surely enhance the capabilities of the proposed approach in terms of both selectivity and sensitivity. In addition, an upgraded version of the software with deconvolution capabilities and also including the use of the

entire isotopic clusters in the screening method instead of the monoisotopic masses of the target species will increase the performance of the method and also will provide enhanced confirmation of the findings based on the isotopic signals, particularly useful for confirmatory purposes on those chemicals containing Cl, S, or Br atom or a large number of C atoms. CONCLUDING REMARKS In this work, a rapid automated screening method for determining pesticide residues in food using LC-TOFMS based on the use of an accurate-mass database has been proposed and evaluated for the analysis of over 60 market-purchased samples obtaining satisfactory results. The accurate-mass database created includes data not only on the accurate masses of the target ions but also the characteristic in-source fragment ions (over 400 fragments included) and retention time data. This information is essential due to the complexity of screening over 300 compounds of similar features in complex matrixes at low concentration levels. This detailed fragmentation information could also be used as a powerful tool for the automatic identification of unknown compounds and/or transformation products with similar structure to known pesticides included in the database. In addition, continuous evolution and improvements of the performance of new instruments in terms of both sensitivity and mass resolution, together with the development of new software tools (i.e., advanced deconvolution software, database screening with isotope pattern recognition, not only monoisotopic masses), will surely increase the potential and ruggedness of the proposed approach. Furthermore, the proposed approach applied here for automated pesticide residue screening in foodstuffs could be further extended in other research fields such as environmental monitoring (priority and emerging contaminants), forensics/toxicology, homeland security, or sport drug testing, demonstrating the flexibility and versatility of LC-TOFMS. ACKNOWLEDGMENT The authors acknowledge funding support from the European Commission, DG SANCO (Specific Agreement No. 2007/1 to Framework Partnership Agreement No. SANCO/2007/FOOD SAFETY/025-Pesticides in Fruit and Vegetable), the Junta de Andalucıá (Regional Government of Andalusia (Spain)) (Research Groups AGR-0159 and FQM323 and Project FQM-01463), and the Spanish “Ministerio de Innovacioń y Ciencia” (Project BQU-200615066). M.M. gratefully acknowledges the “Juan de la Cierva” research contract from the Spanish Ministry of Science and Technology. SUPPORTING INFORMATION AVAILABLE Table S1, with detailed information on the fragmentation and isotopic signatures of the studied pesticides, and Figures S1 and S2, related to the use of the database and the screening method. This material is available free of charge via the Internet at http://pubs.acs.org. Received for review July 8, 2008. Accepted November 25, 2008. AC801411T


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Accurate-Mass Databases for Comprehensive Screening of Pesticide

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